{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from tqdm import tqdm\n",
    "from math import floor\n",
    "from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################\n",
    "# Following are some utilities for tile coding from Rich.\n",
    "# To make each file self-contained, I copied them from\n",
    "# http://incompleteideas.net/tiles/tiles3.py-remove\n",
    "# with some naming convention changes\n",
    "#\n",
    "# Tile coding starts\n",
    "class IHT:\n",
    "    \"Structure to handle collisions\"\n",
    "    def __init__(self, size_val):\n",
    "        self.size = size_val\n",
    "        self.overfull_count = 0\n",
    "        self.dictionary = {}\n",
    "    \n",
    "    def count(self):\n",
    "        return len(self.dictionary)\n",
    "    \n",
    "    def full(self):\n",
    "        return len(self.dictionary) >= self.size\n",
    "\n",
    "    def get_index(self, obj, read_only=False):\n",
    "        d = self.dictionary\n",
    "        if obj in d:\n",
    "            return d[obj]\n",
    "        elif read_only:\n",
    "            return None\n",
    "\n",
    "        size = self.size\n",
    "        count = self.count()\n",
    "        if count >= size:\n",
    "            if self.overfull_count == 0:\n",
    "                print('IHT full, starting to allow collisions')\n",
    "            self.overfull_count += 1\n",
    "            return hash(obj) % self.size\n",
    "        \n",
    "        else:\n",
    "            d[obj] = count\n",
    "            return count\n",
    "def hash_coords(coordinates, m, read_only=False):\n",
    "    if isinstance(m, IHT):\n",
    "        return m.get_index(tuple(coordinates), read_only=read_only)\n",
    "    if isinstance(m, int):\n",
    "        return hash(tuple(coordinates)) % m\n",
    "    \n",
    "    if m is None:\n",
    "        return coordinates\n",
    "\n",
    "def tiles(iht_or_size, num_tilings, floats, ints=None, read_only=False):\n",
    "    \"\"\"returns num-tilings tile indices corresponding to the floats and ints\"\"\"\n",
    "    if ints is None:\n",
    "        ints = []\n",
    "    qfloats = [floor(f * num_tilings) for f in floats]\n",
    "    tiles = []\n",
    "    for tiling in range(num_tilings):\n",
    "        tilingX2 = tiling * 2\n",
    "        coords = [tiling]\n",
    "        b = tiling\n",
    "        for q in qfloats:\n",
    "            coords.append((q + b) // num_tilings)\n",
    "            b += tilingX2\n",
    "        coords.extend(ints)\n",
    "        tiles.append(hash_coords(coords, iht_or_size, read_only))\n",
    "    return tiles\n",
    "# Tile coding ends\n",
    "#######################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# possible priorities\n",
    "PRIORITIES = np.arange(0, 4)\n",
    "# reward for each priority\n",
    "REWARDS = np.power(2, np.arange(0, 4))\n",
    "\n",
    "# possible actions\n",
    "REJECT = 0\n",
    "ACCEPT = 1\n",
    "ACTIONS = [REJECT, ACCEPT]\n",
    "\n",
    "# total number of servers\n",
    "NUM_OF_SERVERS = 10\n",
    "\n",
    "# at each time step, a busy server will be free w.p. 0.06\n",
    "PROBABILITY_FREE = 0.06\n",
    "# probability for exploration\n",
    "EPSILON = 0.1\n",
    "# step size for learning state-action value\n",
    "ALPHA = 0.01\n",
    "# step size for learning average reward\n",
    "BETA = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a wrapper class for differential semi-gradient Sarsa state-action function\n",
    "class ValueFunction:\n",
    "    # In this example I use the tiling software instead of implementing standard tiling by myself\n",
    "    # One important thing is that tiling is only a map from (state, action) to a series of indices\n",
    "    # It doesn't matter whether the indices have meaning, only if this map satisfy some property\n",
    "    # View the following webpage for more information\n",
    "    # http://incompleteideas.net/sutton/tiles/tiles3.html\n",
    "    # @alpha: step size for learning state-action value\n",
    "    # @beta: step size for learning average reward\n",
    "    def __init__(self, num_of_tilings, alpha=ALPHA, beta=BETA, max_size=2048):\n",
    "        self.max_size = max_size\n",
    "        self.num_of_tilings = num_of_tilings\n",
    "        self.alpha = alpha / num_of_tilings\n",
    "        self.beta = beta\n",
    "        self.hash_table = IHT(self.max_size)\n",
    "        self.weights = np.zeros(self.max_size)\n",
    "\n",
    "        # state features needs scaling to satisfy the tile software\n",
    "        self.server_scale = self.num_of_tilings / float(NUM_OF_SERVERS)\n",
    "        self.priority_scale = self.num_of_tilings / float(len(PRIORITIES) - 1)\n",
    "\n",
    "        self.average_reward = 0.0\n",
    "\n",
    "    # get indices of active tiles for given state and action\n",
    "    def get_active_tiles(self, free_servers, priority, action):\n",
    "        active_tiles = tiles(self.hash_table, self.num_of_tilings, \n",
    "                             [self.server_scale * free_servers, self.priority_scale * priority],\n",
    "                             [action])\n",
    "        return active_tiles\n",
    "    \n",
    "    # estimate the value of given state and action without subtracting average\n",
    "    def value(self, free_servers, priority, action):\n",
    "        active_tiles = self.get_active_tiles(free_servers, priority, action)\n",
    "        return np.sum(self.weights[active_tiles])\n",
    "    \n",
    "    # estimate the value of given state without subtracting average\n",
    "    def state_value(self, free_servers, priority):\n",
    "        values = [self.value(free_servers, priority, action) for action in ACTIONS]\n",
    "        # if no free server, can't accept\n",
    "        if free_servers == 0:\n",
    "            return values[REJECT]\n",
    "        return np.max(values)\n",
    "\n",
    "    \n",
    "    # learn with given sequence\n",
    "    def learn(self, free_servers, priority, action, new_free_servers, new_priority, new_action, reward):\n",
    "        active_tiles = self.get_active_tiles(free_servers, priority, action)\n",
    "        estimation = np.sum(self.weights[active_tiles])\n",
    "        delta = reward - self.average_reward + self.value(new_free_servers, new_priority, new_action) - estimation\n",
    "        self.average_reward += self.beta * delta\n",
    "        delta *= self.alpha\n",
    "        for active_tile in active_tiles:\n",
    "            self.weights[active_tile] += delta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# get action based on epsilon greedy policy and @valueFunction\n",
    "def get_action(free_servers, priority, value_function):\n",
    "    # if no free server, can't accept\n",
    "    if free_servers == 0:\n",
    "        return REJECT\n",
    "    if np.random.binomial(1, EPSILON) == 1:\n",
    "        return np.random.choice(ACTIONS)\n",
    "    values = [value_function.value(free_servers, priority, action) for action in ACTIONS]\n",
    "    return np.random.choice([action_ for action_, value_ in enumerate(values) if value_ == np.max(values)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take an action\n",
    "def take_action(free_servers, priority, action):\n",
    "    if free_servers > 0 and action == ACCEPT:\n",
    "        free_servers -= 1\n",
    "    reward = REWARDS[priority] * action\n",
    "    # some busy servers may become free\n",
    "    busy_servers = NUM_OF_SERVERS - free_servers\n",
    "    free_servers += np.random.binomial(busy_servers, PROBABILITY_FREE)\n",
    "    return free_servers, np.random.choice(PRIORITIES), reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# differential semi-gradient Sarsa\n",
    "# @valueFunction: state value function to learn\n",
    "# @maxSteps: step limit in the continuing task\n",
    "def differential_semi_gradient_sarsa(value_function, max_steps):\n",
    "    current_free_servers = NUM_OF_SERVERS\n",
    "    current_priority = np.random.choice(PRIORITIES)\n",
    "    current_action = get_action(current_free_servers, current_priority, value_function)\n",
    "    # track the hit for each number of free servers\n",
    "    freq = np.zeros(NUM_OF_SERVERS + 1)\n",
    "\n",
    "    for _ in tqdm(range(max_steps)):\n",
    "        freq[current_free_servers] += 1\n",
    "        new_free_servers, new_priority, reward = take_action(current_free_servers, current_priority, current_action)\n",
    "        new_action = get_action(new_free_servers, new_priority, value_function)\n",
    "        value_function.learn(current_free_servers, current_priority, current_action,\n",
    "                             new_free_servers, new_priority, new_action, reward)\n",
    "        current_free_servers = new_free_servers\n",
    "        current_priority = new_priority\n",
    "        current_action = new_action\n",
    "    print('Frequency of number of free servers:')\n",
    "    print(freq / max_steps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000000/1000000 [01:42<00:00, 9743.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of number of free servers:\n",
      "[1.29957e-01 2.35554e-01 2.78380e-01 2.10457e-01 1.03673e-01 3.37370e-02\n",
      " 7.12300e-03 9.96000e-04 1.10000e-04 1.00000e-05 3.00000e-06]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x2000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Figure 10.5, Differential semi-gradient Sarsa on the access-control queuing task\n",
    "def figure_10_5():\n",
    "    max_steps = int(1e6)\n",
    "    # use tile coding with 8 tilings\n",
    "    num_of_tilings = 8\n",
    "    value_function = ValueFunction(num_of_tilings)\n",
    "    differential_semi_gradient_sarsa(value_function, max_steps)\n",
    "    values = np.zeros((len(PRIORITIES), NUM_OF_SERVERS + 1))\n",
    "    for priority in PRIORITIES:\n",
    "        for free_servers in range(NUM_OF_SERVERS + 1):\n",
    "            values[priority, free_servers] = value_function.state_value(free_servers, priority)\n",
    "\n",
    "    fig = plt.figure(figsize=(10, 20))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    for priority in PRIORITIES:\n",
    "        plt.plot(range(NUM_OF_SERVERS + 1), values[priority, :], label='priority %d' % (REWARDS[priority]))\n",
    "    plt.xlabel('Number of free servers')\n",
    "    plt.ylabel('Differential value of best action')\n",
    "    plt.legend()\n",
    "\n",
    "    ax = fig.add_subplot(2, 1, 2)\n",
    "    policy = np.zeros((len(PRIORITIES), NUM_OF_SERVERS + 1))\n",
    "    for priority in PRIORITIES:\n",
    "        for free_servers in range(NUM_OF_SERVERS + 1):\n",
    "            values = [value_function.value(free_servers, priority, action) for action in ACTIONS]\n",
    "            if free_servers == 0:\n",
    "                policy[priority, free_servers] = REJECT\n",
    "            else:\n",
    "                policy[priority, free_servers] = np.argmax(values)\n",
    "\n",
    "    fig = sns.heatmap(policy, cmap=\"YlGnBu\", ax=ax, xticklabels=range(NUM_OF_SERVERS + 1), yticklabels=PRIORITIES)\n",
    "    fig.set_title('Policy (0 Reject, 1 Accept)')\n",
    "    fig.set_xlabel('Number of free servers')\n",
    "    fig.set_ylabel('Priority')\n",
    "\n",
    "figure_10_5()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "rl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
